from sklearn_benchmarks.reporting.hp_match import HPMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HPMatchReporting("sklearnex", config="config.yml")
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	NaN	6.759	0.0	-1	1	NaN	0.049	0.000	0.243	0.243	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	NaN	6.757	0.0	-1	5	NaN	0.048	0.000	0.246	0.246	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.013	0.001	NaN	6.318	0.0	1	100	NaN	0.048	0.000	0.263	0.263	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	NaN	6.683	0.0	-1	100	NaN	0.048	0.001	0.250	0.250	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	NaN	6.788	0.0	1	5	NaN	0.048	0.001	0.243	0.243	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	NaN	6.747	0.0	1	1	NaN	0.048	0.000	0.246	0.246	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	NaN	0.304	0.0	-1	1	NaN	0.010	0.000	0.536	0.536	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	NaN	0.318	0.0	-1	5	NaN	0.010	0.000	0.513	0.513	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	NaN	0.316	0.0	1	100	NaN	0.010	0.000	0.525	0.525	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	NaN	0.319	0.0	-1	100	NaN	0.010	0.001	0.495	0.496	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	NaN	0.325	0.0	1	5	NaN	0.010	0.000	0.505	0.505	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	NaN	0.320	0.0	1	1	NaN	0.010	0.000	0.499	0.499	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.065	0.151	NaN	0.000	0.002	-1	1	0.663	0.201	0.004	10.283	10.284	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.025	0.002	NaN	0.000	0.025	-1	1	1.000	0.008	0.000	3.030	3.031	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	3.080	0.044	NaN	0.000	0.003	-1	5	0.757	0.204	0.005	15.068	15.072	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.029	0.006	NaN	0.000	0.029	-1	5	1.000	0.009	0.001	3.254	3.263	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.300	0.010	NaN	0.000	0.002	1	100	0.882	0.249	0.005	9.253	9.254	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.022	0.000	NaN	0.000	0.022	1	100	1.000	0.009	0.000	2.524	2.525	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	3.117	0.046	NaN	0.000	0.003	-1	100	0.882	0.242	0.004	12.856	12.858	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.024	0.002	NaN	0.000	0.024	-1	100	1.000	0.008	0.001	2.855	2.869	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.269	0.012	NaN	0.000	0.002	1	5	0.757	0.204	0.005	11.101	11.103	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.022	0.000	NaN	0.000	0.022	1	5	1.000	0.008	0.000	2.580	2.581	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.240	0.007	NaN	0.001	0.001	1	1	0.663	0.201	0.002	6.178	6.178	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.022	0.004	NaN	0.000	0.022	1	1	1.000	0.008	0.000	2.675	2.675	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.795	0.034	NaN	0.000	0.002	-1	1	0.896	0.031	0.001	57.973	57.996	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.005	0.001	NaN	0.000	0.005	-1	1	1.000	0.001	0.000	5.750	5.772	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.881	0.043	NaN	0.000	0.003	-1	5	0.922	0.033	0.001	87.478	87.494	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.011	0.002	NaN	0.000	0.011	-1	5	1.000	0.001	0.000	13.410	13.475	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.181	0.007	NaN	0.000	0.002	1	100	0.929	0.078	0.002	27.843	27.853	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	NaN	0.000	0.003	1	100	1.000	0.001	0.000	3.780	3.802	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.875	0.038	NaN	0.000	0.003	-1	100	0.929	0.077	0.003	37.181	37.201	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.007	0.003	NaN	0.000	0.007	-1	100	1.000	0.001	0.000	8.111	8.164	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.157	0.006	NaN	0.000	0.002	1	5	0.922	0.036	0.002	60.475	60.608	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	NaN	0.000	0.003	1	5	1.000	0.001	0.000	3.924	3.929	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.120	0.057	NaN	0.000	0.001	1	1	0.896	0.034	0.001	33.266	33.299	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	NaN	0.000	0.002	1	1	1.000	0.001	0.000	2.402	2.409	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.418	0.048	NaN	0.023	0.0	-1	1	NaN	0.858	0.089	3.982	4.004	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.132	0.063	NaN	0.019	0.0	-1	5	NaN	0.805	0.014	5.135	5.136	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.962	0.074	NaN	0.020	0.0	1	100	NaN	0.812	0.019	4.880	4.881	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.117	0.058	NaN	0.019	0.0	-1	100	NaN	0.837	0.049	4.919	4.927	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.203	0.057	NaN	0.019	0.0	1	5	NaN	0.774	0.008	5.432	5.432	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.154	0.092	NaN	0.019	0.0	1	1	NaN	0.808	0.016	5.143	5.144	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	NaN	0.019	0.0	-1	1	NaN	0.003	0.002	0.249	0.283	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.026	0.0	-1	5	NaN	0.002	0.002	0.282	0.344	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.026	0.0	1	100	NaN	0.001	0.001	0.444	0.594	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.026	0.0	-1	100	NaN	0.001	0.000	0.608	0.608	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.027	0.0	1	5	NaN	0.001	0.000	0.530	0.542	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.027	0.0	1	1	NaN	0.001	0.000	0.615	0.617	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.855	1.003	NaN	0.000	0.001	-1	1	0.929	0.112	0.003	7.610	7.612	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	NaN	0.000	0.003	-1	1	1.000	0.000	0.000	10.108	10.342	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.064	0.323	NaN	0.000	0.001	-1	5	0.946	0.206	0.003	5.154	5.155	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.004	0.000	NaN	0.000	0.004	-1	5	1.000	0.000	0.000	7.518	7.805	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	5.744	0.437	NaN	0.000	0.006	1	100	0.951	0.615	0.010	9.335	9.337	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.004	0.001	NaN	0.000	0.004	1	100	1.000	0.001	0.000	4.336	4.456	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.477	0.282	NaN	0.000	0.003	-1	100	0.951	0.601	0.012	5.783	5.784	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.006	0.001	NaN	0.000	0.006	-1	100	1.000	0.001	0.000	6.566	6.740	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.781	0.320	NaN	0.000	0.002	1	5	0.946	0.207	0.004	8.603	8.605	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.000	NaN	0.000	0.002	1	5	1.000	0.000	0.000	3.393	3.493	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.988	0.291	NaN	0.000	0.001	1	1	0.929	0.113	0.002	8.779	8.780	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	NaN	0.000	0.001	1	1	1.000	0.000	0.000	3.590	3.728	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.032	0.013	NaN	0.000	0.000	-1	1	0.891	0.000	0.000	67.826	71.611	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.003	0.000	NaN	0.000	0.003	-1	1	1.000	0.000	0.000	30.963	32.341	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.028	0.001	NaN	0.001	0.000	-1	5	0.911	0.001	0.000	36.746	36.768	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.003	0.001	NaN	0.000	0.003	-1	5	1.000	0.000	0.000	35.403	37.263	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.042	0.007	NaN	0.000	0.000	1	100	0.894	0.005	0.000	7.855	7.855	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	NaN	0.000	0.001	1	100	1.000	0.000	0.000	6.130	6.490	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.044	0.005	NaN	0.000	0.000	-1	100	0.894	0.006	0.001	7.162	7.336	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	NaN	0.000	0.002	-1	100	1.000	0.000	0.000	20.354	21.791	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.023	0.000	NaN	0.001	0.000	1	5	0.911	0.001	0.000	30.305	30.406	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	NaN	0.000	0.001	1	5	1.000	0.000	0.000	6.674	7.059	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.022	0.000	NaN	0.001	0.000	1	1	0.891	0.000	0.000	47.746	47.889	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	NaN	0.000	0.001	1	1	1.000	0.000	0.000	6.841	7.183	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.615	0.125	30	0.026	0.0	random	NaN	0.474	0.033	1.299	1.302	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.657	0.024	30	0.024	0.0	k-means++	NaN	0.520	0.032	1.264	1.267	See	See
6	KMeans_tall	fit	1000000	1000000	100	6.317	0.216	30	0.127	0.0	random	NaN	2.946	0.046	2.144	2.145	See	See
9	KMeans_tall	fit	1000000	1000000	100	6.533	0.095	30	0.122	0.0	k-means++	NaN	3.119	0.028	2.095	2.095	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.0	30	0.009	0.000	random	0.001	0.0	0.0	8.899	13.348	See	See
2	KMeans_tall	predict	1000000	1	2	0.002	0.0	30	0.000	0.002	random	1.000	0.0	0.0	8.646	12.981	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.0	30	0.009	0.000	k-means++	0.001	0.0	0.0	10.843	11.883	See	See
5	KMeans_tall	predict	1000000	1	2	0.002	0.0	30	0.000	0.002	k-means++	1.000	0.0	0.0	13.759	14.678	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.0	30	0.378	0.000	random	0.002	0.0	0.0	7.190	7.561	See	See
8	KMeans_tall	predict	1000000	1	100	0.002	0.0	30	0.001	0.002	random	1.000	0.0	0.0	12.765	13.106	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.0	30	0.397	0.000	k-means++	0.002	0.0	0.0	6.223	6.516	See	See
11	KMeans_tall	predict	1000000	1	100	0.002	0.0	30	0.001	0.002	k-means++	1.000	0.0	0.0	12.282	12.573	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.100	0.003	20	0.002	0.0	random	NaN	0.034	0.002	2.922	2.927	See	See
3	KMeans_short	fit	10000	10000	2	0.267	0.005	20	0.001	0.0	k-means++	NaN	0.099	0.001	2.697	2.697	See	See
6	KMeans_short	fit	10000	10000	100	0.252	0.006	20	0.032	0.0	random	NaN	0.142	0.004	1.779	1.780	See	See
9	KMeans_short	fit	10000	10000	100	0.725	0.020	20	0.011	0.0	k-means++	NaN	0.382	0.008	1.898	1.898	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.007	0.000	random	0.000	0.001	0.0	3.884	3.895	See	See
2	KMeans_short	predict	10000	1	2	0.002	0.0	20	0.000	0.002	random	1.000	0.000	0.0	12.481	13.199	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.007	0.000	k-means++	0.001	0.001	0.0	3.783	3.806	See	See
5	KMeans_short	predict	10000	1	2	0.002	0.0	20	0.000	0.002	k-means++	1.000	0.000	0.0	12.482	13.031	See	See
7	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.266	0.000	random	0.279	0.001	0.0	2.607	2.616	See	See
8	KMeans_short	predict	10000	1	100	0.002	0.0	20	0.000	0.002	random	1.000	0.000	0.0	9.380	9.532	See	See
10	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.268	0.000	k-means++	0.317	0.001	0.0	2.467	2.485	See	See
11	KMeans_short	predict	10000	1	100	0.002	0.0	20	0.000	0.002	k-means++	1.000	0.000	0.0	8.951	9.073	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	11.907	0.445	[20]	0.067	0.000	NaN	NaN	NaN	NaN	NaN	2.112	0.039	5.639	5.640	See	See
3	LogisticRegression	fit	1000	1000	10000	1.007	0.604	[26]	0.079	0.001	NaN	NaN	NaN	NaN	NaN	0.850	0.040	1.185	1.186	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.000	0.0	[20]	1.826	0.0	NaN	NaN	NaN	NaN	0.56	0.008	0.024	0.053	0.159	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.0	[20]	0.013	0.0	NaN	NaN	NaN	NaN	1.00	0.000	0.000	0.312	0.325	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.0	[26]	3.970	0.0	NaN	NaN	NaN	NaN	0.35	0.004	0.000	0.516	0.517	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.0	[26]	0.845	0.0	NaN	NaN	NaN	NaN	0.00	0.001	0.000	0.127	0.127	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.206	0.005	NaN	0.389	0.0	NaN	NaN	NaN	0.203	0.002	1.013	1.013	See	See
3	Ridge	fit	1000000	1000000	100	1.221	0.102	NaN	0.655	0.0	NaN	NaN	NaN	0.353	0.280	3.456	4.412	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.01	0.001	NaN	7.810	0.0	NaN	NaN	0.083	0.017	0.0	0.608	0.608	See	See
2	Ridge	predict	1000	1	10000	0.00	0.000	NaN	1.098	0.0	NaN	NaN	NaN	0.000	0.0	0.592	0.642	See	See
4	Ridge	predict	1000000	1000	100	0.00	0.000	NaN	4.764	0.0	NaN	NaN	1.000	0.000	0.0	0.567	0.709	See	See
5	Ridge	predict	1000000	1	100	0.00	0.000	NaN	0.014	0.0	NaN	NaN	NaN	0.000	0.0	0.603	0.646	See	See